# hyppo: A Multivariate Hypothesis Testing Python Package

arXiv: https://arxiv.org/abs/1907.02088 · HTML: https://mlsanity.com/paper/1907.02088/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2019.

## Built on by (most cited first, 8 of 8)

- [Unifying Pairwise Interactions in Complex Dynamics](https://mlsanity.com/paper/2201.11941/) (2022)
- [From Conditional to Unconditional Independence: Testing Conditional Independence via Transport Maps](https://mlsanity.com/paper/2504.09567/) (2025)
- [Can Large Language Models Implement Agent-Based Models? An ODD-based Replication Study](https://mlsanity.com/paper/2602.10140/) (2026)
- [Testing for context-dependent changes in neural encoding in naturalistic experiments](https://mlsanity.com/paper/2211.09295/) (2022)
- [Learning sources of variability from high-dimensional observational studies](https://mlsanity.com/paper/2307.13868/) (2023)
- [ALPACA: A Reinforcement Learning Environment for Medication Repurposing and Treatment Optimization in Alzheimer's Disease](https://mlsanity.com/paper/2602.19298/) (2026)
- [Mind the Approximation: Fisher-Weighted SVD Compression for ViTs](https://mlsanity.com/paper/2609.07155/) (2026)
- [Memorisation bias in medical AI](https://mlsanity.com/paper/2609.17223/) (2026)

## Abstract (arXiv)

We introduce hyppo, a unified library for performing multivariate hypothesis testing, including independence, two-sample, and k-sample testing. While many multivariate independence tests have R packages available, the interfaces are inconsistent and most are not available in Python. hyppo includes many state of the art multivariate testing procedures. The package is easy-to-use and is flexible enough to enable future extensions. The documentation and all releases are available at https://hyppo.neurodata.io.